Field of the Disclosure
Embodiments of the present disclosure generally relate to adaptive boosting (AdaBoost) classification, and more specifically relate to efficient decision tree traversals in an AdaBoost classifier.
Description of the Related Art
AdaBoost, short for “Adaptive Boosting”, is an algorithm for constructing a strong classifier as a linear combination of weak classifiers such as decision trees. In an AdaBoost classifier, the output of the weak classifiers is combined into a weighted sum that represents the final output of the boosted classifier. AdaBoost is adaptive in the sense that subsequent weak learners are tweaked in favor of those instances misclassified by previous classifiers. AdaBoost in which decision trees are used as the weak learners is often referred to as the best out-of-the-box classifier and is a popular classifier for vision and data analytics. A detailed description of AdaBoost may be found, for example, in Y. Fruend and R. Schapire, “A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting,” Journal of Computer and System Sciences, Vol. 55, Issue 1, August 1997, pp. 119-139.